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Disease Detection Using Soft Computing


Affiliations
1 Research Scholar, DAV University, Jalandhar, India
2 Associate Professor, DAV University, Jalandhar, India
 

Disease detection using soft computing is an emerging field that utilizes various techniques from the domain of artificial intelligence and machine learning to accurately diagnose diseases. Soft computing techniques, such as neural networks, fuzzy logic, and genetic algorithms, are used to build intelligent systems that can analyze complex data and patterns to identify the presence of diseases. In this research paper author has put his efforts to explore the application of soft computing in the diagnosis of disease. Author choose fuzzy logic as the soft computing technique and explore the work done by various researchers for disease diagnosis using fuzzy logic. Author concluded that disease detection using soft computing is a promising area of research that has the potential to transform the field of healthcare. By harnessing the power of artificial intelligence and machine learning, we can improve the accuracy and efficiency of disease diagnosis, leading to better patient outcomes and a healthier society.

Keywords

Soft Computing, Fuzzy Logic, Disease Diagnosis.
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  • . Sheikh, M. B. E., & Al-Jasser, H. F. (2012). A fuzzy expert system for the diagnosis of thyroid diseases. Journal of Medical Systems, 36(6), 3599-3605. https://doi.org/10.1007/s10916-012-9854-8

  • . Ramya, R., & Palanisamy, V. (2016). A fuzzy logic-based approach for tuberculosis diagnosis. International Journal of Computer Applications, 146(7), 39-43.

  • . Mustapha, A., Bakar, S. A., & Abu-Bakar, S. A. R. (2014). A fuzzy logic-based decision support system for breast cancer diagnosis. Expert Systems with Applications, 41(4), 1476-1482.

  • . Marimuthu, R., & Venkatesan, P. (2013). A fuzzy logic-based approach for heart disease diagnosis. International Journal of Computer Applications, 75(16), 8-11.

  • . Naik, N. H., & Raja, K. B. (2013). A hybrid fuzzy logic and artificial neural network approach for diabetes diagnosis. International Journal of Computer Applications, 77(10), 21-26.

  • . Zaidi, S. A., Khan, M. A., & Rizvi, S. M. M. (2012). A fuzzy expert system for the diagnosis of hepatitis B. Journal of Medical Systems, 36(6), 3699-3710.

  • . Salleh, A. M. A., & Wahab, N. A. (2015). A fuzzy logic approach for the diagnosis of dengue fever. Procedia Computer Science, 72, 245-252.

  • . Sheikh, M. B. E., & Fadaei, K. I. (2013). A fuzzy expert system for the diagnosis of glaucoma. Journal of Medical Systems, 37(3), 9918.

  • . Sheikh, M. B. E., & Zarei, M. H. (2013). A fuzzy expert system for the diagnosis of prostate cancer. Journal of Medical Systems, 37(5), 9957.

  • . Akinola, A. T., Adeyemo, A. O., & Soriyan, O. S. (2014). A fuzzy logic-based approach for the diagnosis of Alzheimer's disease. Expert Systems with Applications, 41(6), 3065-3070.

  • . Sheikh, M. B. E., & Al-Jasser, H. F. (2013). A fuzzy expert system for the diagnosis of thyroid diseases. Journal of Medical Systems, 37(4), 9934.


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  • Disease Detection Using Soft Computing

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Authors

Deepak Sharma
Research Scholar, DAV University, Jalandhar, India
Sanjeev Kumar Sharma
Associate Professor, DAV University, Jalandhar, India

Abstract


Disease detection using soft computing is an emerging field that utilizes various techniques from the domain of artificial intelligence and machine learning to accurately diagnose diseases. Soft computing techniques, such as neural networks, fuzzy logic, and genetic algorithms, are used to build intelligent systems that can analyze complex data and patterns to identify the presence of diseases. In this research paper author has put his efforts to explore the application of soft computing in the diagnosis of disease. Author choose fuzzy logic as the soft computing technique and explore the work done by various researchers for disease diagnosis using fuzzy logic. Author concluded that disease detection using soft computing is a promising area of research that has the potential to transform the field of healthcare. By harnessing the power of artificial intelligence and machine learning, we can improve the accuracy and efficiency of disease diagnosis, leading to better patient outcomes and a healthier society.

Keywords


Soft Computing, Fuzzy Logic, Disease Diagnosis.

References